PoFEL: Energy-efficient Consensus for Blockchain-based Hierarchical Federated Learning

TL;DR

This paper introduces PoFEL, a lightweight energy-efficient consensus algorithm for blockchain-based hierarchical federated learning, enhancing security, efficiency, and fairness in distributed model training.

Contribution

It proposes PoFEL, a novel consensus protocol that recycles energy, improves efficiency, and integrates security schemes for blockchain-enabled federated learning.

Findings

Achieves low computational cost and high efficiency.

Ensures fairness and security in model aggregation.

Demonstrates effectiveness through experimental results.

Abstract

Facilitated by mobile edge computing, client-edge-cloud hierarchical federated learning (HFL) enables communication-efficient model training in a widespread area but also incurs additional security and privacy challenges from intermediate model aggregations and remains the single point of failure issue. To tackle these challenges, we propose a blockchain-based HFL (BHFL) system that operates a permissioned blockchain among edge servers for model aggregation without the need for a centralized cloud server. The employment of blockchain, however, introduces additional overhead. To enable a compact and efficient workflow, we design a novel lightweight consensus algorithm, named Proof of Federated Edge Learning (PoFEL), to recycle the energy consumed for local model training. Specifically, the leader node is selected by evaluating the intermediate FEL models from all edge servers instead of other energy-wasting but meaningless calculations. This design thus improves the system efficiency compared with traditional BHFL frameworks. To prevent model plagiarism and bribery voting during the consensus process, we propose Hash-based Commitment and Digital Signature (HCDS) and Bayesian Truth Serum-based Voting (BTSV) schemes. Finally, we devise an incentive mechanism to motivate continuous contributions from clients to the learning task. Experimental results demonstrate that our proposed BHFL system with the corresponding consensus protocol and incentive mechanism achieves effectiveness, low computational cost, and fairness.